Sparse recovery based on the generalized error function

In this paper, we propose a novel sparse recovery method based on the generalized error function. Both the theoretical analysis and the practical algorithms are presented. Numerical experiments are conducted to demonstrate the advantageous performance of the proposed approach over the state-of-the-art sparse recovery methods. Its practical application in magnetic resonance imaging (MRI) reconstruction is studied as well.